# coding:utf-8
# Author : hiicy redldw
# Date : 2019/05/20
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy

# 数据的处理过程
data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

data_dir = r'F:\Resources\DataSets\hymenoptera_data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                          data_transforms[x])
                  for x in ['train', 'val']}
print('img',image_datasets['train'][1])
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
                                             shuffle=True, num_workers=4)
              for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
# 使用gpu
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

def imshow(inp, title=None):
    """Imshow for Tensor."""
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    inp = std * inp + mean
    inp = np.clip(inp, 0, 1)
    plt.imshow(inp)
    if title is not None:
        plt.title(title)
    plt.pause(0.001)  # pause a bit so that plots are updated


def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                scheduler.step()
                #
                model.train()  # Set model to training mode
            else:
                # 就一些层如dropout等没使用
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)  # 复制数据到cpuorgpu
                labels = labels.to(device)

                # zero the parameter gradients
                optimizer.zero_grad()  # 每次数据都清零梯度

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)  # 前面返回值，后面返回索引
                    loss = criterion(outputs, labels)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                running_loss += loss.item() * inputs.size(0)  # 平均损失 * 当前训练数量=当前的总损失
                running_corrects += torch.sum(preds == labels.data)  # 该次训练正确了几个

            epoch_loss = running_loss / dataset_sizes[phase]  # 一次迭代的损失
            epoch_acc = running_corrects.double() / dataset_sizes[phase]  # 一次迭代的正确量

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                phase, epoch_loss, epoch_acc))

            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict()) # 权重
        print()
    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model

def visualize_model(model, num_images=6):
    was_training = model.training
    model.eval()
    images_so_far = 0
    fig = plt.figure()
    # 预测，都不要梯度
    with torch.no_grad():
        for i, (inputs, labels) in enumerate(dataloaders['val']):
            inputs = inputs.to(device)
            labels = labels.to(device)

            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)

            for j in range(inputs.size()[0]):
                images_so_far += 1
                ax = plt.subplot(num_images//2, 2, images_so_far)
                ax.axis('off')
                ax.set_title('predicted: {}'.format(class_names[preds[j]]))
                imshow(inputs.cpu().data[j])

                if images_so_far == num_images:
                    model.train(mode=was_training)
                    return
        model.train(mode=was_training)

if __name__ == "__main__":
    # # Get a batch of training data
    # inputs, classes = next(iter(dataloaders['train']))

    # # Make a grid from batch
    # out = torchvision.utils.make_grid(inputs)

    # imshow(out, title=[class_names[x] for x in classes])

    # 微调卷积
    pthfile=r"D:\hiicy\Google\resnet18-5c106cde.pth"
    """
    model_ft = models.resnet18(pretrained=False)
    model_ft.load_state_dict(torch.load(pthfile))
    num_ftrs = model_ft.fc.in_features
    model_ft.fc = nn.Linear(num_ftrs, 2)  # 硬改网络层

    model_ft = model_ft.to(device) # 移植模型到gpu上运算如果有gpu

    criterion = nn.CrossEntropyLoss()

    # Observe that all parameters are being optimized
    optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)

    # Decay LR by a factor of 0.1 every 7 epochs
    exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

    model_ft = train_model(model_ft,criterion,optimizer_ft,exp_lr_scheduler,num_epochs=25)
    """

    # ConvNet作为固定特征提取器
    # 我们需要冻结除最后一层之外的所有网络。我们需要设置 requires_grad == False 冻结参数，以便在 backward() 中不计算梯度
    model_conv = models.resnet18(pretrained=False)
    model_conv.load_state_dict(torch.load(pthfile))
    for param in model_conv.parameters():
        # 冻结该层参数,反向传播时不变
        param.requires_grad=False
    num_ftrs = model_conv.fc.in_features
    model_conv.fc = nn.Linear(num_ftrs, 2)
    model_conv = model_conv.to(device)
    criterion = nn.CrossEntropyLoss()
    # 只改fc层的参数
    optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)
    # Decay LR by a factor of 0.1 every 7 epochs
    exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)
    model_conv = train_model(model_conv, criterion, optimizer_conv,
                             exp_lr_scheduler, num_epochs=25)